Developing and managing petroleum resources often entails committing large economic investments over many years with an expectation of receiving correspondingly large financial returns. Whether a petroleum reservoir yields profit or loss depends largely upon the strategies and tactics implemented for reservoir development and management. Due to the inaccuracy of logging tools and unpredictable natural variations in geometry and geological parameters, there is considerable uncertainty as to the detailed characterization of a hydrocarbon reservoir. This uncertainty, coupled with the dramatic variations in the market value of hydrocarbon production, has heightened the importance of financial factors and risk management in reservoir strategies so as to maximize reservoir value. As such, reservoir development planning involves devising and/or selecting strong strategies and tactics that will yield favorable economic results over the long term.
Reservoir risk management involves optimizing assets given inherent reservoir uncertainties and minimizing risk by reducing these uncertainties. Optimizing assets usually involves making decisions about technologies and strategies (such as advanced completion systems, drilling a new well, setting injection or production target rates, etc.) and quantifying the value of implementing the proposed technology in the presence of physical and financial uncertainties. Physical uncertainty includes uncertainty in the type of reservoir model used and the properties used to populate the model. Financial uncertainties refer to uncertainty in the financial variables associated with the asset, such as the discount rate, hydrocarbon price, etc.
The optimization process thus should be stochastic, with a risk level (or alternatively a confidence level) associated with the optimized solution. Based on the cost-benefit analysis (cost of implementing the technology versus the gain or value from the implementation) and the associated risk level, a decision may be made on implementing the technology.
Minimizing the risk level involves gathering information about the reservoir to reduce or eliminate the inherent uncertainties. The cost of gathering this information should be balanced against the value the information brings to the stochastic optimization process. Valuation of information can then guide decisions on implementing monitoring technologies for uncertainty reduction.
Typical subsurface reservoir value of information concepts (e.g., decision trees) are limited in the number of variables that can be handled (say 1-3) and are not suitable for spatial variables such as reservoir properties at a particular location.
Thus, there exists a need in the art for a method of handling more variables when evaluating information for reservoir risk management. Ideally, such a method would also allow for incorporation of many data types and uncertainties to fully describe the system being evaluated.